CPDD: Clustering and Probabilistic based Data Dissemination in Vehicular Adhoc Networks

Abstract

Objectives: To maintain Quality of Service (QOS) for data dissemination, prevent packet loss and high reachability. Methods: To achieve this, CPDD: Clustering and Probabilistic based Data Dissemination in Vehicular Adhoc Networks is proposed. The messages are disseminated between the clusters. The Performance metrics like throughput, jitter, Normalized Routing Load, Routing Overhead and packet loss ratio are analysed using NS2 and the results are compared for the varying number of nodes. Findings: The proposed approach has a low packet drop ratio and end-to-end delay compared to SECADD, SEAD and S1PD. The efficiency of the traffic system and the quality of driving are both improved by data dissemination through VANETs by using CPDD. The goal of an effective clustering method is to reduce the number of clusters while maintaining high communication over the vehicular network. Novelty: Establishing effective communication between two fastmoving vehicles is a major challenge in VANET. The challenge in designing a robust and reliable message dissemination technique is required for VANET safety applications. The results of the proposed model CPDD is compared for different numbers of nodes. The proposed work improves the QOS metrics with the Normalized Routing Load of 1.009%, Routing Overhead of 1.03 bits/sec, throughput of 205.5 bits/sec, end-to-end delay of 0.435 seconds and packet loss ratio of 20.02% for 50 nodes.

A Multi-Agent Deep Reinforcement Learning-Based Popular Content Distribution Scheme in Vehicular Networks

“…Sun et al [15] proposed a collaborative multicast scheme that combined multicast with D2D-assisted relay for transmitting multimedia data in vehicular networks, and designed a communication quality metric and a relay selection algorithm to optimize data transmission. Jeevitha and Bhuvaneswari [16] proposed a cluster-based cooperative multicast scheme for efficient multimedia data dissemination in hybrid cellular-D2D vehicular networks. The scheme first clustered the multicast group members using cellular signalling, then selected the cooperative forwarding set using graph theory algorithms, and finally used D2D communication to achieve both intra-cluster and inter-cluster multicast.…”